code: def __init__(
      self,
      decoder_config=None,
      **kwargs,
  ):
      super().__init__()
      self.config = decoder_config
  
      self.input_size = self.config.input_size
      self.fallback_label = self.config.fallback_label
      self.str2idx = self.config.str2idx
      self.vocab_size = len(self.config.str2idx)
  
      # Create Matcher object to perform matching on the decoded output
      self.matcher = Matcher(self.config.match)
  
      # Tokenizer
      self.tokenizer_type = self.config.tokenizer
      self.pretrained_model_name_or_path = (
          self.config.pretrained_model_name_or_path
      )
      self.vocab_file = self.config.vocab_file
  
      # Load tokenizer required for decoding the output from the generate
      # function of the text input feature for LLMs.
      self.tokenizer = get_tokenizer(
          self.tokenizer_type,
          self.vocab_file,
          self.pretrained_model_name_or_path,
      )
  
imports: import logging
  import re
  from typing import Any, Dict, List, Union
  import torch
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import CATEGORY, LOGITS, PREDICTIONS, PROBABILITIES, TEXT
  from ludwig.decoders.base import Decoder
  from ludwig.decoders.registry import register_decoder
  from ludwig.decoders.utils import extract_generated_tokens
  from ludwig.schema.decoders.llm_decoders import CategoryExtractorDecoderConfig, TextExtractorDecoderConfig
  from ludwig.utils.strings_utils import get_tokenizer
label_desc: Use image node_id 1 to create a new CategoryExtractorDecoder object from inherited base classes: Decoder with example: obj = CategoryExtractorDecoder(decoder_config)